Computational method for identifying adhesin and adhesin-like proteins of therapeutic potential

ABSTRACT

A computational method for identifying adhesin and adhesin-like proteins, said method comprising steps of computing the sequence-based attributes of a neural network software wherein the attributes are (i) amino acid frequencies, (ii) multiplet frequency, (iii) dipeptide frequencies, (iv),charge composition, and (v) hydrophobic composition, training the artificial neural Network (ANN) for each of the computed five attributes, and identifying the adhesin and adhesin-like proteins having probability of being an adhesin (P ad ) as ≧0.51; a computer system for performing the method; and genes and proteins encoding adhesin and adhesin-like proteins.

CROSS-REFERENCE TO OTHER APPLICATIONS

The present application claims the benefit of priority of each of the following applications: U.S., Provisional Application No. 06/589,220 filed on Jul. 20, 2004 and Indian Patent Application No. 173/DEL/2004 dated Feb. 6, 2004. The text of each of the aforementioned applications is specifically incorporated herein by reference.

FIELD OF THE PRESENT INVENTION

A computational method for identifying adhesin and adhesin-like proteins; computer system for performing the method; and genes and proteins encoding adhesin and adhesin-like proteins.

BACKGROUND AND PRIOR ART OF THE PRESENT INVENTION

The progress in genome sequencing projects has generated a large number of inferred protein sequences from different organisms. It is expected that the availability of the information on the complete set of proteins from infectious human pathogens will enable us to develop novel molecular approaches to combat them. A necessary step in the successful colonization and subsequent manifestation of disease by microbial pathogens is the ability to adhere to host cells.

Microbial pathogens encode several proteins known as adhesins that mediate their adherence to host cell surface receptors, membranes, or extracellular matrix for successful colonization. Investigations in this primary event of host-pathogen interaction over the past decades have revealed a wide array of adhesins in a variety of pathogenic microbes. Presently, substantial information on the biogenesis of adhesins and the regulation of adhesin factors is available. One of the best understood mechanisms of bacterial adherence is attachment mediated by pili or fimbriae. Several afimbrial adhesins also have been reported. In addition, limited knowledge on the target host receptors also has been gained (Finlay, B. B. and Falkow, S 1997).

New approaches to vaccine development focus on targeting adhesins to abrogate the colonization process (Wizemann, et al 1999). However, the specific role of particular adhesins has been difficult to elucidate. Thus, prediction of adhesins or adhesin-like proteins and their functional characterization is likely to aid not only in deciphering the molecular mechanisms of host pathogen interaction but also in developing new vaccine formulations, which can be tested in suitable experimental model systems.

One of the best understood mechanisms of bacterial adherence is attachment mediated by pili or fimbriae. For example, FimH and PapG adhesins of Escherichia coli (Maurer, L., Orndorff, P.(1987), Bock, K., et al.(1985). Other examples of pili group adhesins include type IV pili in Pseudomonas aeruginosa, Neisseria species, Moraxella species, Enteropathogenic Escherichia coli and Vibrio cholerae (Sperandio V et al (1996). Several afimbrial adhesins are HMW proteins of Haemophilus influenzae (van Schilfgaarde 2000), the filamentous hemagglutinin, pertactin, of Bordetella pertussis (Bassinet et al 2000), the BabA of H. pylori (Yu J et al 2002) and the YadA adhesin of Yersinia enterocolitica (Neubauer et al 2000). The intimin receptor protein (Tir) of Enteropathogenic E. coli (EPEC) is another type of adhesin (Ide T et al 2003). Other class of adhesins includes MrkD protein of Kleibsella pneumoniae, Hia of H. influenzae (St Geme et al 2000), Ag I/II of Streptococcus mutans and SspA, SspB of Streptococcus gordonii (Egland et al 2001), FnbA, FnbB of Staphylococcus aureus and SfbI, protein F of Streptococcus pyogenes, the PsaA of Streptococcus pneumoniae (De et al 2003).

A known example of adhesins approved as vaccine is the acellular pertussis vaccine containing FHA and pertactin against B. pertussis the causative agent of whooping cough (Halperin, S et al 2003). Immunization with FimH is being evaluated for protective immunity against pathogenic E. coli (Langermann S et al 2000), in Streptococcus pneumoniae, PsaA is being investigated as a potential vaccine candidate against pneumococcal disease (Rapola, S et al 2003). Immunization results with BabA adhesin showed promise for developing a vaccine against H. pylori (Prinz, C et al 2003). A synthetic peptide sequence anti-adhesin vaccine is being evaluated for protection against Pseudomonas aeruginosa infections.

Screening for adhesin and adhesin like proteins by conventional experimental method is laborious, time consuming and expensive. As an alternative, homology search is used to facilitate the identification of adhesins. Although, this procedure is useful in the analysis of genome organization (Wolf et al 2001) and of metabolic pathways. (Peregrin-Alvarez et al 2003, Rison et al 2002), it is somewhat limited in allowing functional predictions when the homologues are not functionally characterized or the sequence divergence is high. Assignment of functional roles to proteins based on this technique has been possible for only about 60% of the predicted protein sequences (Fraser et al 2000). Thus, we explored the possibility of developing a non-homology method based on sequence composition properties combined with the power of the Artificial Neural Networks to identify adhesins and adhesin-like proteins in species belonging to wide phylogenetic spectrum.

Twenty years ago, Nishikawa et al carried out some of the early attempts to classify proteins into different groups based on compositional analysis (Nishikawa et al 1983). More recently, the software PropSearch was developed for analyzing protein sequences where conventional alignment tools fail to identify significantly similar sequences (Hobohm, U. and Sander, C 1995). PropSearch uses 144 compositional properties of protein sequences to detect possible structural or functional relationships between a new sequence and sequences in the database. Recently the compositional attributes of proteins have been used to develop softwares for predicting secretory proteins in bacteria and apicoplast targeted proteins in Plasmodium falciparum by training Artificial Neural Networks (Zuegge et al 2001).

Zuegge et al have used the 20 amino acid compositional properties. Their objective was to extract features of apicoplast targeted proteins in Plasmodium falciparum. This is distinct from our software SPAAN that focuses on adhesins and adhesin-like proteins involved in host-pathogen interaction.

Hobohm and Sander have used 144 compositional properties including isoelectric point and amino acid and dipeptide composition to generate hypotheses on putative functional role of proteins that are refractory to analysis using other sequence alignment based approaches like BLAST and FASTA. Hobohm and Sander do not specifically address the issue of adhesins and adhesin-like proteins, which is the focus of SPAAN. Nishikawa et al had originally attempted to classify proteins into various functional groups. This was a curiosity driven exercise but eventually lead to the development of a software to discriminate extra-cellular proteins from intracellular proteins. This work did not address the issue of adhesins and adhesin-like proteins, which is the focus of SPAAN.

Thus, none of the aforementioned research groups have been able to envisage the methodology of the instant application. The inventive method of this application provides novel proteins and corresponding gene sequences.

Adhesins and adhesin-like proteins mediate host-pathogen interactions. This is the first step in colonization of a host by microbial pathogens. Attempts Worldwide are focused on designing vaccine formulations comprising adhesin proteins derived from pathogens. When immunized, host will have its immune system primed against adhesins for that pathogen. When a pathogen is actually encountered, the surveillance mechanism will recognize these adhesins, bind them through antigen-antibody interactions and neutralize the pathogen through complement mediate cascade and other related clearance mechanisms. This strategy has been successfully employed in the case of Whooping cough and is being actively pursued in the case of Pneumonia, Gastric Ulcer and Urinary tract infections.

OBJECTS OF THE PRESENT INVENTION

The main object of the present invention is to provide a computational method for identifying adhesin and adhesin-like proteins of therapeutic potential.

Another object of invention is to provide a method for screening the proteins with unique compositional characteristics as putative adhesins in different pathogens.

Yet, another object of the invention is providing the use of gene sequences encoding the putative adhesin proteins useful as preventive therapeutics.

SUMMARY OF THE PRESENT INVENTION

A computational method for identifying adhesin and adhesin-like proteins, said method comprising steps of computing the sequence-based attributes of protein sequences using five attribute modules of software SPAAN, (i) amino acid frequencies, (ii) multiplet frequency, (iii) dipeptide frequencies, (iv) charge composition, and (v) hydrophobic composition, training the artificial neural Network (ANN) for each of the computed five attributes, and identifying the adhesin and adhesin-like proteins having probability of being an adhesin (P_(ad)) as ≧0.51; a computer system for performing the method; and genes and proteins encoding adhesin and adhesin-like proteins

DETAILED DESCRIPTION OF THE PRESENT INVENTION

Accordingly, the present invention relates to a computational method for identifying adhesin and adhesin-like proteins, said method comprising steps of computing the sequence-based attributes of protein sequences using five attribute modules of software SPAAN, (i) amino acid frequencies, (ii) multiplet frequency, (iii) dipeptide frequencies, (iv) charge composition, and (v) hydrophobic composition, training the artificial neural Network (ANN) for each of the computed five attributes, and identifying the adhesin and adhesin-like proteins having probability of being an adhesin (P_(ad)) as ≧0.51; a computer system for performing the method; and genes and proteins encoding adhesin and adhesin-like proteins

In an embodiment of the present invention, wherein the invention relates to a computational method for identifying adhesin and adhesin-like proteins, said method comprising steps of:

-   -   a. computing the sequence-based attributes of protein sequences         using five attribute modules of a neural network software,         wherein the attributes are software, (i) amino acid         frequencies, (ii) multiplet frequency, (iii) dipeptide         frequencies, (iv) charge composition, and (v) hydrophobic         composition,     -   b. training the artificial neural Network (ANN) for each of the         computed five attributes, and     -   c. identifying the adhesin and adhesin-like proteins having         probability of being an adhesin (P_(ad)) as ≧0.51.

In another embodiment of the present invention, wherein the invention relates to a method wherein the protein sequences is obtained from pathogens, eukaryotes, and multicellular organisms.

In an embodiment of the present invention, wherein the invention relates to a method, wherein the protein sequences are obtained from the pathogens selected from a group of organisms comprising Escherichia coli, Haemophilus influenzae, Helicobacter pylori, Mycoplasma pneumoniae, Mycobacterium tuberculosis, Rickettsiae prowazekii, Porphyromonas gingivalis, Shigella flexneri, Streptococcus mutans, Streptococcus pneumoniae, Neisseria men ingitides, Streptococcus pyogenes, Treponema pallidum and Severe Acute Respiratory Syndrome associated human coronavirus (SARS).

In yet another embodiment of the present invention, wherein the method of the invention is a non-homology method.

In still another embodiment of the present invention, wherein the invention relates to the method using 105 compositional properties of the sequences.

In still another embodiment of the present invention, wherein the invention relates to a method showing sensitivity of at least 90%.

In still another embodiment of the present invention, wherein the invention relates to the method showing specificity of 100%.

In still another embodiment of the present invention, wherein the invention relates to a method identifying adhesins from distantly related organisms.

In still another embodiment of the present invention, wherein the invention relates to the neural network has multi-layer feed forward topology, consisting of an input layer, one hidden layer, and an output layer.

In still another embodiment of the present invention, wherein the invention relates to the number of neurons in the input layer are equal to the number of input data points for each attribute.

In still another embodiment of the present invention, wherein the invention relates to the “P_(ad)” is a weighted linear sum of the probabilities from five computed attributes. In still another embodiment of the present invention, wherein the invention relates to each trained network assigns a probability value of being an adhesin for the protein sequence.

In still another embodiment of the present invention, wherein the invention relates to a computer system for performing the method of claim 1, said system comprising a central processing unit, executing SPAAN program, giving probabilities based on different attributes using Artificial Neural Network and in built other programs of assessing attributes, all stored in a memory device accessed by CPU, a display on which the central processing unit displays the screens of the above mentioned programs in response to user inputs; and a user interface device.

In still another embodiment of the present invention, wherein the invention relates to a set of 274 annotated genes encoding-adhesin and adhesin-like proteins, having SEQ ID Nos. 385 to 658.

In still another embodiment of the present invention, wherein the invention relates to a set of 105 hypothetical genes encoding adhesin and adhesin-like proteins, having SEQ. ID Nos. 659 to 763.

In still another embodiment of the present invention, wherein the invention relates to a set of 279 annotated adhesin and adhesin-like proteins of SEQ ID Nos. 1 to 279.

In still another embodiment of the present invention, wherein the invention relates to a set of 105 hypothetical adhesin and adhesin-like proteins of SEQ ID Nos. 280 to 384.

One more embodiment of the present invention, wherein the invention also relates to a fully connected multilayer feed forward Artificial Neural Network based on the computational method as claimed in claim 1, comprising of an input layer, a hidden layer and an output layer which are connected in the said sequence, wherein each neuron is a binary digit number and is connected to each neuron of the subsequent layer for identifying adhesin or adhesin like proteins, wherein the program steps comprise: [a] feeding a protein sequence in FASTA format; [b] processing the sequence obtained in step [a] through the 5 modules named A, C, D, H and M, wherein attribute. A represents an amino acid composition, attribute C represents a charge composition, attribute D represents a dipeptide composition of the 20 dipeptides [NG, RE, TN, NT, GT, TT, DE, ER, RR, RK, RI, AT, TS, IV, SG, GS, TG, GN, VI and HR], attribute H represents a hydrophobic composition and attribute M represents amino acid frequencies in multiplets to quantify 5 types of compositional attributes of the said protein sequence to obtain numerical input vectors respectively for each of the said attributes wherein the sum of numerical input vectors is 105; [c] processing of; the numerical input vectors obtained in step [b] by,the input neuron layer to obtain signals, wherein the number of neurons is equal to the number of numerical input vectors for each attribute; [d] processing of signals obtained from step [c] by the hidden layer to obtain synaptic weighted signals, wherein the optimal number of neurons in the hidden layer was determined through experimentation for minimizing the error at the best epoch for each network individually; [e] delivering synaptic weighted signals obtained from step [d] to the output layer for assigning of a probability value for each protein sequence fed in step [a] as being an adhesin by each network module; [f] using the individual probabilities obtained from step [e] for computing the final probability of a protein sequence being an adhesin denoted by the P_(ad) value, which is a weighted average of the individual probabilities obtained from step [e] and the associated fraction of correlation which is a measure of the strength of the prediction.

In still another embodiment of the present invention, wherein the input neuron layer consists of a total of 105 neurons corresponding to 105 compositional properties.

In still another embodiment of the present invention, wherein the hidden layer comprises of neurons represented as 30 for amino acid frequencies, 28 for multiplet frequencies, 28 for dipeptide frequencies, 30 for charge composition and 30 for hydrophobic composition.

In still another embodiment of the present invention, wherein the output layer comprises of neurons to deliver the output values as probability value for each protein sequence.

Identification of novel adhesins and their characterization are important for studying host-pathogen interactions and testing new vaccine formulations. We have employed Artificial Neural Networks to develop an algorithm SPAAN (Software for Prediction of Adhesin and Adhesin-like proteins using Neural Networks) that can identify adhesin proteins using 105 compositional properties of a protein sequence. SPAAN could correctly predict well characterized adhesins from several bacterial species and strains. SPAAN showed 89% sensitivity and 100% specificity in a test data set that did not contain proteins in the training set. Putative adhesins identified by the software can serve as potential preventive therapeutics.

The present invention provides a novel computational method for identifying adhesin and adhesin-like proteins of therapeutic potential. More particularly, the present invention relates to candidate genes for these adhesins. The invention further provides new leads for development of candidates genes, and their encoded proteins in their functional relevance to preventive approaches. This computational method involves calculation of several sequence attributes and their subsequent analyses lead to the identification of adhesin proteins in different pathogens. Thus, the present invention is useful for identification of the adhesin proteins in pathogenic organisms. The adhesin proteins from different genomes constitute a set of candidates for functional characterization through targeted gene disruption, microarrays and proteomics. Further, these proteins constitute a set of candidates for further testing in development of preventive therapeutics. Also, are provided the genes encoding the candidate adhesin proteins.

The present method offers novelty in the principles used and the power of Neural Networks to identify new adhesins compared to laborious and time consuming conventional methods. The present method is based on compositional properties of proteins instead of sequence alignments. Therefore this method has the ability to identify adhesin and adhesin like proteins from bacteria belonging to a wide phylogenetic spectrum. The predictions made from this method are readily verifiable through independent analysis and experimentation. The invention has the potential to accelerate the development of new preventive therapeutics, which currently requires high investment in terms of requirement of skilled labor and valuable time.

The present invention relates to a computational method for the identification of candidate adhesin proteins of therapeutic potential. The invention particularly describes a novel method to identify adhesin proteins in different genomes of pathogens. These adhesin proteins can be used for developing preventive therapeutics.

Accordingly, a computational method for identifying adhesin and adhesin-like proteins of therapeutic potential which comprises calculation of 105 compositional properties under the five sequence attributes, namely, Amino Acid frequency, Multiplet frequency, dipeptide frequency, charge composition and hydrophobic composition; and then training Artificial Neural Network (ANN, Feed Forward Error Back Propagation) using these properties for differentiating between adhesin and non-adhesin class of proteins. This computational method involves quantifying 105 compositional attributes of query proteins and qualifying them as adhesins or non-adhesins by a P_(ad) value (Probability of being an adhesin). The present invention is useful for identification of adhesin and adhesin-like proteins in pathogenic organisms. These newly identified adhesin and adhesin-like proteins constitute a set-of candidates for development of new preventive therapeutics that can be tested in suitable experimental model systems readily. In addition, the genes encoding the candidate adhesin and adhesin-like proteins are provided.

The invention provides a set of candidate adhesin and adhesin-like proteins and their coding genes for further evaluation as preventive therapeutics. The method of invention is based on the analysis of protein sequence attributes instead of sequence patterns classified to functional domains. Present method is less dependent on sequence relationships and therefore offers the potential power of identifying adhesins from distantly related organisms. The invention provides a computational method, which involves prediction of adhesin and adhesin-like proteins using Artificial Neural Networks. The proteins termed adhesin were found to be predicted with a high probability (P_(ad) ≧0.51) in various pathogens. Some adhesin sequences turned out to be identical or homologous to proteins that are antigenic or implicated in virulence. By this approach, proteins could be identified and short-listed for further testing in development of new vaccine formulations to eliminate diseases caused by various pathogenic organisms.

DESCRIPTION OF TABLES

Table 1: Output file format given by SPAAN.

Table 2: Organism Name, Accession number, Number of base pairs, Date of release and Total number of proteins.

Table 3. Prediction of well characterized adhesins from various bacterial pathogens using SPAAN.

Table 4. Analysis of predictions made by SPAAN on genome scans of a few selected pathogenic organisms.

Table 5: GI numbers and Gene IDs of new putative adhesins predicted by SPAAN in the genomes listed in Table 2.

Table 6: GI numbers and Gene IDs of hypothetical proteins predicted as putative adhesins by SPAAN in the genomes listed in Table 2.

Table 7: The list of 198 adhesins found in bacteria

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

FIG. 1, shows the Neural Network architecture

FIG. 2 shows assessment of SPAAN using defined test dataset.

FIG. 3.(a) shows Histogram plots of the number of proteins in the various P_(ad) value ranges are shown. (b) Pairwise sequence relationships among the adhesins were determined using CLUSTAL W and plotted on X-axis. Higher scores indicate similar pairs. (c) plot for non-adhesins. Data are plotted in the 4 quadrant format for clear inspection.

Software program was written in C Language and operated on Red Hat Linux 8.0 operating system. The computer program accepts input protein sequences in FastA format and produces a tabulated output. The output Table contains one row for each protein listing the probability outputs of each of the five modules, a weighted average probability of these five modules (P_(ad)), and the function of the protein as described in the input sequence file. This software is called SPAAN (A Software for Prediction of Adhesins and Adhesin-like proteins using Neural Networks) and a software copyright has been filed. Although this software has multiple modules, the running of these modules have been integrated and automated. The user only needs to run one command.

AAcompo.c:

Input: File containing protein sequences in the fasta format.

Output: File containing frequencies of all 20 AAs for each protein in one row.

charge.c:

Input: File containing protein sequences in the fasta format.

Output: File containing frequency of charged amino acids (R, K, E and D) and moments (up to 18th order) of the positions of charged amino acids.

hdr.c:

Input: File containing protein sequences in the fasta format.

Output: File containing frequencies of 5 groups of amino acids formed on the basis their Hydrophobicity and moments of their positions up to 5th order.

multiplets.c:

Input: File containing protein sequences in the fasta format.

Output: File containing fractions of multiplets of each of the 20 amino acids.

quetydipep.c:

Input: File. 1 containing protein sequences in the fasta format.

-   -   File.2 containing list of the significant dipeptides in         dipeptide analysis.

Output: File containing frequencies of the dipeptides listed in the input File.2 for each protein in the input File. 1.

train.c:

Input: File containing following specifications

-   -   1. Number of input and output parameters.     -   2. Number of nodes in the hidden layers.     -   3. Names of the training, validate and test data files.     -   4. Learning rate, coefficient of moment.     -   5. Maximum number of cycles for training.

Output: Outputs are as follows.

-   -   1. Output of the trained NN for the test data set.     -   2. Values of the weight connections in the trained NN.     -   3. Some extra information about training.

recognize.c:

Input: File containing following specifications

-   -   1. Number of input and output parameters.     -   2. Number of nodes in the hidden layers.     -   3. Names of the query input file.     -   4. Name of the file containing values of the weight connections         for trained NN.     -   5. Name of the output file.

Output: Outputs for the query entries calculated by the trained NN.

standard.c:

Input: File containing protein sequences in fasta format.

Output: File containing protein sequences in fasta format with all the new line characters removed lying within a sequence.

filter.c:

Input: File containing protein sequences in fasta format.

Output: File containing protein sequences from the input except those which are short in length (<50 AAs) and which contain any amino acid other than the 20 known amino acids.

The Five Attributes:

Amino Acid Frequencies

Amino acid frequency f_(i)=(counts of ith amino acid in the sequence)/1; i, =1 . . . 20, 1 is the length of the protein.

Multiplet Frequency

Multiplets are defined as homopolymeric stretches (X)_(n) where X is any of the 20 amino acids and n is an integer >2. After identifying all the multiplets, the frequencies of the amino acids in the multiplets were computed as f _(i)(m)=(counts of i ^(th) amino acid occurring as multiplet)/1 Dipeptide Frequencies

The frequency of a dipeptide (i, j) f_(ij)=(counts of ij^(th) dipeptide)/(total dipeptide counts); i, j ranges from 1 to 20.

It has been found that dipeptide repeats in proteins are important for functional expression of the clumping factor present on Staphylococcus aureus cell surface that binds to fibrinogen (Hartford et al 1999). Thus we included the dipeptide frequency module. The total number of dipeptides is 400. For optimal training of Neural Network, the ratio of total number of input vectors to the total number of weight connections must be around 2 to avoid over fitting (Andrea et al). Therefore, we identified the dipeptides whose frequencies in the adhesin data set (469 proteins, see database construction) were significantly different from that in the non-adhesin dataset (703 proteins) using t-test. The frequencies of top 20 dipeptides (when arranged in the descending order of the p-values of t-test), were fed to the Neural Network. These dipeptides were (using single letter IUPAC-IUB code) NG, RE, TN, NT, GT, TT, DE, ER, RR, RK, RI, AT, TS, IV, SG, GS, TG, GN, VI, AND HR. With frequency inputs for 20 dipeptides and 28 neurons in the 2nd layer, the total number of weight connections is 588, and is in keeping with the criterion of avoiding over fitting.

Charge Composition

The input frequency of charged amino acids (R, K, E and D considering the ionization properties of the side chains at pH 7.2) given by f_(c)=(counts of charged amino acids)/1 Further, information on the characteristics of the distribution of the charged amino acids in a given protein sequence was provided by computing the moments of the positions of the occurrences of the charged amino acids. Since moments characterize the patterns of distribution such as skewness and kurtosis (sharpness of the peak) we have used them to represent the distribution patterns of the charged residues in the sequence.

The general expression to compute moments of a given order; say ‘i’ is $M_{r} = {{r^{th}\quad{order}\quad{moment}\quad{of}\quad{the}\quad{positions}\quad{of}\quad{charged}\quad{amino}\quad{acids}}\text{}\quad = {\sum\frac{\left( {X_{i} - X_{m}} \right)^{r}}{N}}}$

Where, X_(m)=mean of all positions of charged amino acids

-   -   X_(i)=position of i^(th) charged amino acid     -   N=number of charged amino acids in the sequence

The moments 2^(nd) to 19^(th) order were used to train the ANN constituting a total 20 inputs in addition to frequency of charged amino acids and the length of the protein. The upper limit of 19^(th) order was set based on assessments of sensitivity and specificity on a small dataset of adhesins and non-adhesins. Moments of order greater than 19 were not useful in improvement of performance.

Hydrophobic Composition

A given protein sequence was digitally transformed using the hydrophobic scores of the amino acids according to Brendel et al. (43). The scores for five groups of amino acids: (−8 for K, E, D, R), (−4 for S, T, N, Q), (−2 for P, H), (+1 for A, G, Y, C, W), (+2 for L, V, I, F, M).

Following inputs were given for each of the group (a) f_(i)=(counts of i^(th) group)/(total counts in the protein); i ranges from 1 to 5 (b) m_(ji)=j^(th) order moment of positions of amino acids in i^(th) group; j ranges from 2 to 5.

A total of 25 inputs representing the hydrophobic composition of a protein were fed to the Neural Network. The rationale for using moments was same as described in the section on charge composition inputs.

Taken together a total of 105 compositional properties of a given protein sequence Were used to predict their adhesin characteristics.

The software PropSearch uses 144. compositional properties of protein sequences to detect possible structural or functional relationships between a new sequence and sequences in the database (Hobohm and Sander 1995). The approach defines protein sequence dissimilarity (or distance) as a weighted sum of differences of compositional properties such as singlet and doublet amino acid composition, molecular weight, isoelectric point (protein property search or PropSearch). Compositional properties of proteins have also been used for predicting secretory proteins in bacteria and apicoplast targeted proteins in Plasmodium falciparum (Zuegge, et al. 2001). The properties used here are statistical methods, principal component analysis, self-organizing maps, and supervised neural networks. In SPAAN, we have used 105 compositional properties in the five modules viz. Amino Acid frequencies, Multiplet frequencies, Dipeptide frequencies, Charge composition, Hydrophobic composition. The total of 105 properties used in SPAAN are 20 for Amino acid frequencies, 20 for Multiplets frequencies, 20 for Dipeptide frequencies (Top 20 significant dipeptides are used, based on t-test), 20 for Charge composition (frequency of charged amino acids (R, K, E and D) and moments of 2nd to 19th order), and 25 for Hydrophobic composition (Amino acids were classified into five groups (−8 for K, E, D, R), (−4 for S, T, N, Q), (−2 for P, H), (+1 for A, G, Y, C, W), (+2 for L, V, I, F, M). A total of 25 inputs consisted of the following: Frequency of each group, Moments of positions of amino acids in each group from 2nd to 5th order.

Neural Network

A feed forward error back propagation Neural Network was used. The program is a kind gift from Charles W. Anderson, Department of Computer Science, Colorado State University, Fort Collins, Colo. 80523, anderson@cs.colostate.edu

Neural Network Architecture

The Neural Network used here has a multi-layer feed-forward topology. It consists of an input layer, one hidden layer and an output layer. This is a ‘fully-connected’ Neural Network where each neuron i is connected to each unit j of the next layer (FIG. 1). The weight of each-connection is denoted by w_(ij). The state I_(i) of each neuron in the input layer is assigned directly from the input data, whereas the states of hidden layer neurons are computed by the sigmoid function, h _(j)=1/(1+exp −(w _(j0) +Σw _(ij) I _(i))), where, w_(j0) is the bias weight

The back propagation algorithm was used to minimize the differences between the computed output and the desired output. Ten thousand cycles (epochs) of iterations are performed. Subsequently, the best epoch with minimum error was identified. At this point the network produces approximate target values for a given input in the training set.

A network was trained optimally for each attribute. Thus five networks were prepared. The schematic diagram (FIG. 1) shows the procedure adopted. The number of neurons in the input layer was equal to the number of input data points for each attribute (for example 20 neurons for 20 numerical input vectors of the amino acid composition attribute). The optimal number of neurons in the hidden layer was determined through experimentation for minimizing the error at the best epoch for each network individually. An upper limit for the total number of weight connections was set to half of the total number of input vectors to avoid over fitting as suggested previously (Andrea et al).

Computer programs to compute individual compositional attributes were written in C and executed on a PC under Red Hat Linux ver 7.3 or 8.0. The network was trained on the training set, checks error and optimizes using the validate set through back propagation. The validate set was different from the training set. Since, the number of well annotated adhesins were not many, we used the ‘validate set’ itself as test set for preliminary evaluation of the performance and to obtain the fraction of correlation to compute the weighted average probability (P_(ad) value) described in the next section. The training set had 367 adhesins and 580 non-adhesins. The validate set had 102 adhesins and 123 non-adhesins. The adhesins were qualified with a digit ‘1’ and the non-adhesins were qualified with a digit ‘0’.

During predictions, the network is fed with new data from the sequences that were not part of training set. Each network assigns a probability value of being an adhesin to a given sequence. The final probability is computed as described in the next section.

Probability of being an Adhesin, the P_(ad) Value

Query proteins are processed modularly through network trained for each attribute. Thus, five probability outputs are obtained. Final prediction was computed using the following expression which is a weighted linear sum of the probabilities from five modules: $P_{ad} = \frac{\left( {{P_{A}*{fc}_{A}} + {P_{C}*{fc}_{C}} + {P_{D}*{fc}_{D}} + {P_{H}*{fc}_{H}} + {P_{M}*{fc}_{M}}} \right)}{\left( {{fc}_{A} + {fc}_{C} + {fc}_{D} + {fc}_{H} + {fc}_{M}} \right)}$ P_(i)=Probability from i module, fc_(i)=fraction of correlation of i module of the trained Neural Network,

Where i=A (Amino acid frequencies), C (Charge composition), D (Dipeptide frequencies), H (Hydrophobic composition), or M (Multiplet frequencies).

The fraction of correlation fc_(i) represents the fraction of total entries that were correctly predicted. (P_(i,adhesin)>0.5 and P_(i, non-adhesin)<0.5) by the trained network on the test set used in preliminary evaluation (Charles Anderson).

Neural Network

A feed forward error back propagation Neural Network was used. The program was downloaded from the web site with permission from the author, Charles W. Anderson, Department of Computer Science, Colorado State University, Fort Collins, Colo. 80523, anderson@cs.colostate.edu

Statistical Analysis

All statistical procedures were carried out using Microsoft Excel (Microsoft Corporation Inc. USA).

Sequence Analysis

Homology analysis was carried out using CLUSTAL W (Thompson et al 1994),. BLAST (Altschul et al 1990), CDD (conserved domain database) search (Marchler-Bauer et al 2002).

The whole genome sequences of microbial pathogens present new opportunities for the development of clinical applications such as diagnostics and vaccines. The present invention provides new leads for the development of candidate genes, and their encoded proteins in their functional relevance to preventive therapeutics.

The protein sequences of both the classes, i.e. adhesin and non-adhesin, were downloaded from the existing database (National Centre for Biotechnology Information (NCBI), USA). A total of 105 compositional properties under the five sequence attributes namely, amino acid composition, multiplet composition, dipeptide composition, charge composition and hydrophobic composition were computed,-by computer programs written in C language. The attributes were computed for all the proteins in both the databases. The sequence-based attributes were then used to train Artificial Neural Network for each of the protein attributes. Adhesins were qualified,by the digit ‘1’ and non-adhesins were qualified by the digit ‘0’. Finally each trained Artificial Neural Network was used to identify potential adhesins which can be envisaged to be useful for the development of preventive therapeutics against pathogenic infections. Accordingly, the invention provides a computational method for identifying adhesin and adhesin-like proteins of therapeutic potential, which comprises:

1. preparing two comprehensive data-sets of adhesin and non-adhesin proteins from publicly available information on protein sequences,

2. calculating computationally the sequence based attributes of the protein sequences in the publicly available protein datasets using specially developed Software for Prediction of Adhesins and Adhesin-like proteins using Neural Networks (SPAAN),

3. training the Artificial Neural Network (ANN) for the selected attributes,

4. assigning probability value suitable for an adhesin, “P_(ad)” to the query protein and identifying adhesin like property in the query proteins with the help of trained Artificial Neural Network implemented in SPAAN,

5. validating computationally the protein sequences as therapeutic potentials by comparing with the known protein sequences that are biochemically characterized in the pathogen genome.

In an embodiment of the invention the protein sequence data may be taken from an organism, specifically but not limited to organisms such as Escherichia coli, Haemophilus influenzae, Helicobacter pylori, Mycoplasma pneumoniae, Mycobacterium tuberculosis, Rickettsiae prowazekii, Porphyromonas gingivalis, Shigella flexneri, Streptococcus mutans, Streptococcus pneumoniae, Neisseria meningitides, Streptococcus pyogenes, Treponema pallidum, Severe Acute Respiratory Syndrome associated coronavirus.

In another embodiment to the present invention different sequence-based attributes used for identification of proteins of therapeutic potential, comprise amino acid composition, charge composition, hydrophobicity composition, multiplets frequencies, and dipeptide frequencies.

In an embodiment, the non-homologous adhesin protein sequence may be compared with that of known sequences of therapeutic applications in the selected pathogens.

In an embodiment of the invention, the sequences of adhesin or adhesin like proteins comprise sequences of sequences IDs listed in Tables 5 and.6 identified by the method of invention.

Another embodiment of the invention the computer system comprises a central processing unit, executing SPAAN program, giving probabilities based on different attributes using Artificial Neural Network and in built other programs of assessing attributes, all stored in a memory device accessed by CPU, a display on which the central processing unit displays the screens of the above mentioned programs in response to user inputs; and a user interface device.

In One embodiment of the present invention, the particulars of the organisms such as their name, strain, accession number in NCBI database and other details are given in Table 2:

The invention is further explained with the help of the following examples, which are given by illustration and should be construed to limit the scope of the present invention in any manner.

EXAMPLE 1

Operating SPAAN:

The purpose of the program is to computationally calculate various sequence-based attributes of the protein sequences.

The program works as follows:

The internet downloaded FASTA format files obtained from http://www.ncbi.nlm.nih.gov were saved by the name <organism_name>.faa are converted in the standard format by C program and passed as input to another set of C programs which computes the 5 different attributes of protein sequences (a total of 105 compositional properties in all 5 modules).

The computed properties were fed as input to the 5 different Neural Networks. Each trained network assigns a probability value of being an adhesin for a query protein. The final probability (P_(ad)) was calculated as weighted average of these five individual probabilities. The weights were determined from a correlation value of correct; prediction during test runs of each of the five modules.

Input/Output format:

Downloaded Files and their format:

-   -   <organism name>.faa: file which stores the annotation and the         protein sequence.

Input file Format: FASTA

-   -   “>gi.vertline.”<annotation>

For example,

>gi.vertline.2314605.vertline.gb.vertline.AAD08472.vertline.histidine and glutamine-rich protein MAHHEQQQQQQANSQHHHHHHHAHHHHYYGGEHHHHNAQQHAEQQAEQQ AQQQQQQQAHQQQQQKAQQQNQQY

>gi.vertline.3261822.vertline.gnl.vertline.PID.vertline.e328405 PE_PGRS MIGDGANGGPGQPGGPGGLLYGNGGHGGAGAAGQDRGAGNSAGLIGNGGA GGAGGNGGIGGAGAPGGLGGDGGKGGFADEFTGGFAQGGRGGFGGNGNTG ASGGMGGAGGAGGAGGAGGLLIGDGGAGGAGGIGGAGGVGGGGGAGGTGG GGVASAFGGGNAFGGRGGDGGDGGDGGTGGAGGARGAGGAGGAGGWLSGH SGAHGAMGSGGEGGAGGGGGARGEAGAGGGTSTGTNPGKAGAPGTQGDSG DPGPPG

>gi.vertline . . . . TABLE 1 Output file format given by SPAAN <organism_name>.out SN P_(A) P_(C) P_(D) P_(H) P_(M) P_(ad)-value Protein Name 1 0.05683 0.290803 0.441338 0.50304 0.029503 0.260485 >gi.vertline.32454344.vertline. gb.vertline.AAP82966.1. vertline.orf1a polyprotein [SARS coronavirus Hong Kong ZY-2003] 2 0.639235 0.166721 0.054583 0.935385 0.453498 0.462452 >gi.vertline.32454345.vertline. gb.vertline.AAP82967.1. vertline.orf1ab polyprotein [SARS coronavirus Hong Kong ZY-2003] 3 0.651111 0.911504 0.438696 0.543944 0.924044 0.690247 >gi.vertline.32454346.vertline. gb.vertline.AAP82968.1. vertline.spike glycoprotein [SARS coronavirus Hong Kong ZY-2003] 4 0.464324 0.655003 0.179503 0.008700 0.241573 0.300970 >gi.vertline.32454347.vertline. gb.vertline.AAP82969.1. vertline.Orf3a [SARS coronavirus Hong Kong ZY-2003]

Where P_(A), P_(C), P_(D), P_(H), P_(M) are the outputs of the five Neural Networks.

EXAMPLE 2 Organisms and Sequence Numbers

TABLE 2 Organism Name, Accession number, Number of base pairs, Date of release and Total number of proteins analyzed Accession Number of Total Organism Name Number base pairs Date of release no. of proteins E. coli O157 H7 NC_002695 5498450 7-Mar-2001 5361 H. influenzae Rd NC_000907 1830138 30-Sep-1996 1709 H. pylori J99 NC_000921 1643831 10-Sep-2001 1491 M. pneumoniae NC_000912 816394 2-Apr-2001 689 M. tuberculosis H37Rv NC_000962 4411529 7-Sep-2001 3927 R. prowazekii strain Madrid E NC_000963 1111523 10-Sep-2001 835 P. gingivalis W83 NC_002950 2343476 9-Sep-2003 1909 S. flexneri 2a str. 2457T NC_004741 4599354 23-Apr-2003 4072 S. mutans UA159 NC_004350 2030921 25-Oct-2002 1960 S. pneumoniae R6 NC_003098 2038615 6-Sep-2001 2043 N. meningitidis NC_003116 2184406 27-Sep-2001 2065 serogroup A strain Z2491 S. pyogenes MGAS8232 NC_003485 1895017 Jan 31, 2002 1845 T. pallidum subsp. NC_000919 1138011 7-Sep-2001 1036 pallidum str. Nichols Severe Acute AY291315 29727 11-JUN-2003 14 Respiratory Syndrome (SARS) associated coronavirus Frankfurt 1 SARS coronavirus HSR1 AY323977 29751 15-OCT-2003 14 SARS coronavirus ZJ01 AY297028 29715 19-MAY-2003 3 SARS coronavirus TW1 AY291451 29729 14-MAY-2003 11 SARS coronavirus AY282752 29736 07-MAY-2003 4 CUHK-Su10 SARS coronavirus Urbani AY278741 29727 12-AUG-2003 12 SARS coronavirus NC_004718 29751 9-Sep-2003 29 SARS coronavirus Tor2 AY274119 29751 16-MAY-2003 15 SARS coronavirus GD01 AY278489 29757 18-AUG-2003 12 SARS coronavirus CUHK-W1 AY278554 29736 31-JUL-2003 11 SARS coronavirus BJ01 AY278488 29725 01-MAY-2003 11

EXAMPLE 3

The multi-layered feed forward Neural Network architecture implemented in SPAAN (FIG. 1). A given protein sequence in FASTA format is first processed through the 5 modules A, C, D, H, and M to quantify the five types of compositional attributes. A: Amino acid composition, C: Charge composition, D: dipeptide composition of the 20 dipeptides (NG, RE, TN, NT, GT, TT, DE, ER, RR, RK, RI, AT, TS, IV, SG, GS, TG, GN, VI, HR), H: Hydrophobic composition, M: Amino acid frequencies as Multiplets. The sequence shown is part of the FimH precursor (gi 5524634) of E. coli. Subsequently, these numerical data are input to the input neuron layer. The directions of arrows show data flow. The number of neurons chosen in the input layer was equal to the number of the numerical input vectors of each module. The network was optimally trained through minimization of error of detection based on validate set through back propagation. The details are described in the methods. Each network module assigns a probability value of the protein being an adhesin based on the corresponding attribute. The final probability of a protein sequence being an adhesin is the P_(ad) value a weighted average of the individual probabilities and the associated fraction of correlation which is a measure of the strength of the prediction.

EXAMPLE 4

Performance of SPAAN assessed using a test set of 37 adhesins and 37 non-adhesins that were not part of the training set. Matthew's correlation coefficient (Mcc, plotted on Y-axis) for all the proteins with P_(ad) values above a given threshold (plotted on X-axis) (FIG. 2). The Matthew's correlation is defined as: ${Mcc} = \frac{\left( {{TP}*{TN}} \right) - \left( {{FP}*{FN}} \right)}{\sqrt{\left( {{TN} + {FN}} \right)\left( {{TN} + {FP}} \right)\left( {{TP} + {FN}} \right)\left( {{TP} + {FP}} \right)}}$

Where TP=True Positives, TN=True Negatives, FP=False Positives, FN=False Negatives.

Here TPs are adhesins, TNs are non-adhesins. In general, adhesins have high P_(ad) value, whereas non-adhesins have low P_(ad) value. Thus known adhesins with P_(ad) value above a given threshold are true positives whereas known non-adhesins with P_(ad) value below the given threshold are true negatives. The sensitivity, Sn is given by $\left( \frac{TP}{{TP} + {FN}} \right)$ and specificity, Sp is given by $\left( \frac{TP}{{TP} + {FP}} \right).$ False negatives are those cases, wherein a known adhesin had P_(ad) value lower than the chosen threshold. Similarly, a known non-adhesin with a P_(ad) value higher than the chosen threshold was taken as false positive. A theoretical polynomial curve of second order (dashed line) was fitted to the observed curve (smooth line) with a Karl-Pearson correlation coefficient R²=0.9799. The maximum point of the theoretical curve (where first derivative vanishes and second derivative is negative) was chosen as reference (vertical dotted line) to identify the maximum Mcc=0.94 on the observed curve (shown by arrow). The corresponding P_(ad) value threshold was 0.51. At this P_(ad) value threshold, Sn and Sp were 0.89 and 1.0 respectively. Note that the Mcc does not drop down to the x-axis because the highest P_(ad) value attained by adhesins was 0.939 in comparison to the theoretical attainable limit of 1.0.

EXAMPLE 5

Assessment of SPAAN on well known adhesins from various bacterial pathogens. TABLE 3 Prediction of well characterized adhesins from various bacterial pathogens using SPAAN. Disease P_(ad) value^(b) Species caused Adhesin^(a) Host ligand (Range) E. coli Diarrhoea PapG (27) α-D-gal(1-4) β-D-Gal- 0.84-0.76 containing receptors SfaS (5) alpha-sialyl-beta-2,3-b- 0.94-0.94 galactose FimH (63) D-mannosides 0.96-0.23^(c) Intimin (12) tyrosine-phosphorylated 0.95-0.78 form of host cell receptor Hp90 PrsG (5) Gal(alpha1-4)Gal 0.86-0.85 Nontypeable H. influenza Influenza HMW1, Human epithelial cells 0.97 HMW2 Hia (8) human conjuctival cells 0.93-0.90 H. influenzae bacterial HifE (18) Sialylyganglioside-GM1 0.85-0.73 meningitis^(d) K. pneumoniae Pneumonia MrkD type V collagen 0.82 B. pertussis Whooping FHA Sulphated sugars on cell- 0.85 cough surface glycoconjugates Pertactin Integrins 0.43 Y. enterocolitica Enterocolitis YadA (5) β₁ integrins 0.88-0.79 S. mutans Dental SpaP (2) Salivary glycoprotein 0.88, 0.87 Caries PAc Salivary glycoprotein 0.88 Streptococcus Oral cavity SspA (2) Salivary glycoprotein 0.85, 0.84 gordonii CshA Fibronectin 0.78 CshB Fibronectin 0.63 ScaA Co-aggregation 0.71 SspB (2) Salivary glycoprotein 0.85, 0.84 Streptococcus Tooth decay SpaA Salivary glycoprotein 0.89 sobrinus PAg (2) Salivary glycoprotein 0.89, 0.73 Streptococcus Scarlet Protein F Fibronectin 0.49 pyogenes Fever Streptococcus Bacterial PsaA (5) Human nasopharyngeal 0.82-0.78 pneumoniae Pneumonia cells CbpA^(e)/ phosphorylcholine of the 0.81-0.49 SpsA/ teichoic acid. PbcA/PspC Streptococcus Valve FimA Salivary glycoprotein fibrin 0.76 parasanguis endocarditis Streptococcus Tooth Decay SsaB Salivary glycoprotein 0.71 sanguis Enterococcus Empyma in EfaA Unknown 0.83 faecalis patients with liver disease Staphylococcus Food FnbA Fibronectin 0.8 aureus Poisoning FnbB (3) Fibronectin 0.78, 0.77, 0.69 Helicobacter Peptic BabA (17) difucosylated Lewis^(b) blood 0.87-0.68 pylori Ulcers group antigen ^(a)The number of sequences from different strains and homologs from related species analyzed are shown in parantheses. ^(b)Rounded off to the second decimal. ^(c)Out of 63 FimH proteins, 54 were from E. coli, 6 from Shigella flexineri, 2 from Salmonella enterica and 1 was from Salmonella typhimurium. Except 2 FimH proteins, the rest had P_(ad) ≧ 0.51. The 2 exceptions (gi numbers: 5524636, 1778448) were from E. coli. # The gi: 5524636 protein is annotated as a FimH precursor but is much shorter (129 amino acids) than other members of the family. The gi: 1778448 protein is a S. typhimurium homolog in E. coli. ^(d)Other ailments include pneumonia, epiglottitis, osteomyelitis, septic arthritis and sepsis in infants and older children. ^(e)The adhesin CbpA is also known by alternative names SpsA, PbcA and PspC. A total of seven sequences were analyzed. Except 1 PspC sequence, the rest all had P_(ad) ≧ 0.51.

EXAMPLE 6

Ability of SPAAN to discriminate adhesins from non-adhesins at P_(ad) ≧0.51 (FIG. 3-a).

EXAMPLE 7

The non-homology character of SPAAN assesses in both adhesins and non-adhesins (FIG. 3 b and 3 c).

FIG. 3(a-c). SPAAN is non-homology based software. A total of 130 adhesins and 130 non-adhesins were analyzed to assess whether the predictive power of SPAAN could be influenced by the sequence relationships. (a) Histogram plots of the number of proteins in the various P_(ad) value ranges are shown. Shaded bars represent adhesins whereas open bars represent non-adhesins. Note the SPAAN's ability to segregate adhesins and non-adhesins into two distinct cohesive groups. (b) Pairwise sequence relationships among the adhesins were determined using CLUSTAL W and plotted on X-axis. Higher scores indicate similar pairs. The corresponding differences in P_(ad) values in the same protein pair was plotted on the Y-axis. Each point in the diagram represents a pair. Arrow points to protein pairs of the FimH family with high ΔP_(ad) values in spite of high similarity. Since one of the FimH proteins (gi: 5524636) had very low P_(ad) value all pairs with this false negative protein show high ΔP_(ad) values. The protein (gi: 5524636) is of much shorter length compared with other members of the same family. (c) plot for non-adhesins. Data are plotted in the 4 quadrant format for clear inspection. Note that among protein pairs with CLUSTAL W score <20 the majority (82% in adhesins and 86% in non-adhesins) have ΔP_(ad)<0.2. These data support the non-homology character of SPAAN.

EXAMPLE 8

Genomescan of pathogens by SPAAN identifies well known adhesins and new adhesins and adhesin-like proteins TABLE 4 Analysis of predictions made by SPAAN on genome scans of a few selected pathogenic organisms^(a) Species Escherichia Mycobacterium SARS associated Protein coli tuberculosis corona virus Class O157:H7 H37Rv (11 strains) Total number of 575 435 5 proteins with P_(ad) ≧ 0.51 Known adhesins  17^(b) — — Putative proteins with  92^(c) 105^(j) — adhesin like characteristics Hypothetical proteins  22^(d) — — with adhesin-like characteristics Proteins likely to be 190^(e) 191^(k) 5^(m) extracytoplasmic or located at surface Phage proteins  30^(f) — — Others  13^(g)  6^(l) — Hypothetical proteins 157^(h)  86^(h) — Wrong predictions  54^(i)  47^(i) — ^(a)SPAAN has general applicability. The three pathogens chosen here are those in which intense investigations are being conducted presently. M. tuberculosis is of special importance to developing countries. ^(b)Fimbrial adhesins, AidA-I, gamma intimin, curlin, translocated intimin receptor, putative adhesin and transport, Iha, prepilin peptidase dependent protein C. ^(c)These proteins have been annotated as proteins with a putative function. These sequences were analyzed using CDD (Conserved domain database, NCBI) and BLAST searches. Adhesin like domains were found in these proteins. ^(d)These proteins have been annotated as ‘hypothetical’. These sequences were analyzed using CDD and BLAST searches. Adhesin like domains were found in these proteins. ^(e)These proteins are outer membrane, extracellular, transport, surface, exported, flagellar, periplasmic lipoprotein, and proteins annotated as ‘hypothetical’ but found to have similar functions listed here using BLAST and CDD searches. ^(f)The phage proteins were of the following functional roles - tail fiber, head decoration, DNA injection, tail, major capsid, host specificity, endolysin. ^(g)Proteins predicted by SPAAN but not readily classifiable into the classes listed here have been collectively grouped as ‘Others’. However, some of these proteins are known to participate in host-pathogen interactions. # The annotated functional roles are typeIII secretion, antibiotic resistance, heat shock, acid shock, structural, tellurium resistance, terminase, Hcp-like, Sec-independent translocase, uncharacterized nucleoprotein, HicB-like. ^(h)These proteins have been annotated as hypothetical. Re-analyses of these proteins using BLAST and CDD failed identify any function for these proteins. ^(i)These proteins have been annotated with functional roles that are very likely to occur within the cell. Hence these proteins may have remote possibility of functioning as adhesins or adhesin-like proteins. # Therefore this set of proteins have been incorrectly predicted as adhesins or adhesin-like by SPAAN. ^(j)These proteins are PE_PGRS, PE proteins. Several reports (for example Brennan et al.) indicate that PE_PGRS proteins may be localized to cell surface and aid in host-pathogen interaction. ^(k)Lipoproteins (lpp, lpq, lpr), PPE, outer membrane, surface, transport, secreted, periplasmic, extracellular, ESAT-6, peptidoglycan binding, exported, mpt (with extracellular domains), and proteins annotated as ‘hypothetical’ but found to have similar functions listed here using BLAST and CDD searches. ^(l)These proteins were of the following functions - glutaredoxin-like thioltransferase, putative involvement in molybdate uptake, ATP synthase chain, sulphotransferases, S. erythraea rhodanese-like protein M29612|SERCYSA_5, unknown function. ^(m)These proteins were the spike glycoprotein with antigenic properties, and nsp2, nsp5, nsp6 and nsp7.

TABLE 5 New putative adhesins predicted by SPAAN in the genomes listed in table 2 - (Total number = 279) Protein GI Number Gene ID Protein name Escherichia coli O157:H7 13360742 912619 hemagglutinin/hemolysin-related protein 13362986 914770 putative ATP-binding component of a transport system 13361114 913228 putative tail fiber protein 13364757 913676 minor fimbrial subunit/D-mannose specific adhesin 13362687 915687 putative fimbrial-like protein 13360856 912599 AidA-I adhesin-like protein 13364140 915374 putative fimbrial protein 13359793 914435 putative invasin 13364768 913650 putative invasin 13364034 915471 Gamma intimin 13362703 915668 putative DNA transfer protein precursor 13364141 915376 putative fimbrial protein 13359819 914463 AidA-I adhesin-like protein 13360480 917768 putative fimbrial-like protein 13362692 915681 putative fimbrial-like protein 13362585 916824 putative ATP-binding component of a transport system 13359881 914526 putative flagellin structural protein 13361579 917311 putative type 1 fimbrial protein precursor 13360880 913991 curlin major subunit CsgA 13364036 915465 translocated intimin receptor Tir 13360740 912615 putative major pilin protein 13361582 917317 putative ATP-binding component of a transport system and adhesin protein 13364754 913683 export and assembly outer membrane protein of type 1 fimbriae 13360484 917767 homolog of Salmonella FimH protein 13364751 913688 major type 1 subunit fimbrin 13359597 913742 putative fimbrial protein 13362550 916787 putative ATP-binding component of a transport system 13359595 913739 putative fimbrial protein 13359599 913748 probable outer membrane porin protein involved in fimbrial assembly 13363900 915704 putative fimbrial protein precursor 13361575 917307 putative fimbrial-like protein 13364756 913678 fimbrial morphology 13363496 916142 truncated putative fimbrial protein 13359601 913761 putative fimbrial-like protein 13364145 915368 putative type 1 fimbrial protein 13363902 915708 putative outer membrane usher protein precursor 13361576 917309 putative outer membrane protein 13361013 913353 putative major tail subunit 13364755 913682 fimbrial morphology 13360738 912793 putative outer membrane usher protein 13363928 915608 alpha-amylase 13363495 916144 putative outer membrane protein 13362383 916617 putative type-1 fimbrial protein 13364373 914972 outer membrane vitamin B12 receptor protein BtuB 13360879 912479 minor curlin subunit precursor CsgB 13360739 912756 putative chaperone protein 13361574 917314 putative fimbrial-like protein 13361127 913212 outer membrane protease precursor 13363210 916442 putative lipoprotein 13361104 913238 major tail protein 13361709 917446 putative major tail subunit 13359725 914366 outer membrane pore protein PhoE 13360875 913765 curli production assembly/transport component CsgF 13362170 913927 putative outer membrane protein 13361473 917203 putative BigB-like protein 13364025 915286 EspF protein 13360081 916982 outer membrane receptor for ferric enterobactin (enterochelin) and colicins B and D 13362977 914779 hypothetical lipoprotein 13360351 917632 outer membrane protein X 13360696 914208 putative outer membrane precursor 13361456 917206 putative outer membrane protein 13361626 917374 putative outer host membrane protein precursor 13361698 917449 putative outer membrane protein 13362186 913421 putative outer membrane protein precursor 13362697 915676 long-chain fatty acid transport protein FadL 13360918 914188 flagellar hook protein FlgE 13360737 912506 putative outer membrane protein 13360342 917629 putative outer membrane receptor for iron transport 13363396 916248 outer membrane channel TolC 13361958 912705 putative scaffolding protein in the formation of a murein- synthesizing holoenzyme 13359921 914566 nucleoside-specific channel-forming protein TSX 13360944 913890 outer membrane receptor for ferric iron uptake 13359998 914644 putative outer membrane transport protein 13363390 916251 putative ferrichrome iron receptor precursor 13364227 915153 outer membrane phospholipase A 13361982 912846 putative outer membrane protein 13360129 917032 a minor lipoprotein 13361817 912692 putative outer membrane protein 13360233 917507 membrane spanning protein TolA 13362837 915218 putative outer membrane lipoprotein 13362328 912985 putative colanic acid biosynthesis glycosyl transferase Haemophilus influenzae Rd 16272254 949521 prepilin peptidase-dependent protein D 16272928 950762 immunoglobin A1 protease 16272129 951072 lipoprotein 16273251 950616 hemoglobin-binding protein 30995429 950130 opacity protein 16272854 949634 protective surface antigen D15 16272283 950648 opacity associated protein 16272604 949701 hemoglobin-binding protein Helicobacter pylori J99 4155101 889167 putative vacuolating cytotoxin (VacA) paralog 4154798 890022 putative vacuolating cytotoxin (VacA) paralog 4155426 890036 putative vacuolating cytotoxin (VacA) paralog 4155390 890075 vacuolating cytotoxin 4155400 890058 outer membrane protein - adhesin 4155681 889718 putative Outer membrane protein 4155420 890042 Outer membrane protein/porin 4155775 889799 outer membrane protein - adhesin 4155419 890044 Outer membrane protein/porin 4154526 889066 putative Outer membrane protein 4154724 889419 putative Outer membrane protein 4155862 890404 putative Outer membrane protein 4156048 889958 putative IRON(III) DICITRATE TRANSPORT PROTEIN 4154510 889297 putative Outer membrane protein 4155432 889515 putative outer membrane protein 4155623 889671 putative Outer membrane protein 4155700 889739 putative Outer membrane function 4154740 889426 Outer membrane protein/porin 4155692 889743 putative Outer membrane protein 4155594 889648 putative outer membrane protein 4155680 889719 putative Outer membrane protein 4155217 890243 putative Outer membrane protein 4155958 889905 putative Outer membrane protein 4155201 890259 putative Outer membrane protein 4155013 889232 cag island protein 4154974 889032 putative Outer membrane protein 4155214 890244 putative Outer membrane protein 4154973 889042 Outer membrane protein 4155344 890115 putative Outer membrane protein 4155099 889160 FLAGELLIN A 4155023 888978 cag island protein 4155035 889201 cag island protein, CYTOTOXICITY ASSOCIATED IMMUNODOMINANT ANTIGEN 4155289 890164 NEURAMINYLLACTOSE-BINDING HEMAGGLUTININ PRECURSOR Mycoplasma pneumoniae 13507881 877207 involved in cytadherence 13507880 877268 ADP1_MYCPN adhesin P1 13508228 877211 species specific lipoprotein 13508181 877124 species specific lipoprotein 13508179 877071 Mollicute specific lipoprotein, MG307 homolog, from M. genitalium 13508178 877118 Mollicute specific lipoprotein, MG307 homolog, from M. genitalium, 13508176 876797 Mollicute specific lipoprotein, MG307 homolog, from M. genitalium 13508175 876848 Mollicute specific lipoprotein, MG307 homolog, from M. genitalium 13508106 876953 involved in cytadherence 13508350 877112 similar to phosphate binding protein Psts Mycobacterium tuberculosis H37 Rv 15607496 886491 PPE 15607445 886592 PPE 15610644 888270 PE_PGRS 15608588 886605 PE_PGRS 15609627 887941 PE_PGRS 15610643 888256 PE_PGRS 15607718 887725 PE_PGRS 15609054 885362 PPE 15610486 888113 PPE 15610483 888120 PPE 15610479 888033 PPE 15609771 888573 PE_PGRS 15610648 888306 PE_PGRS 15610481 888114 PE_PGRS 15608117 885264 PE_PGRS 15607973 885391 PE_PGRS 15608231 885258 PE_PGRS 15608906 885429 PE_PGRS 15608891 885544 PPE 15609990 888171 PE_PGRS 15609055 885506 PPE 15608227 887094 PE_PGRS 15610524 888151 PE_PGRS 15609490 886003 PPE 15607886 888664 PE_PGRS 15609624 887909 PE_PGRS 15607420 886621 PE_PGRS 15608897 885325 PE_PGRS(wag22) 15608590 886595 PE_PGRS 15609728 887992 PE_PGRS 15608012 885742 PE_PGRS 15608534 886745 PE_PGRS 15608940 885730 PE_PGRS 15607887 888662 PE_PGRS 15609235 888312 PE_PGRS 15610694 887822 PPE 15609533 885517 PE_PGRS 15610480 PE_PGRS Rickettsia prowazekii strain Madrid E 15604316 883411 CELL SURFACE ANTIGEN (sca3) 15604546 883694 CELL SURFACE ANTIGEN (sca5) Porphyromonas gingivalis W83 34541453 2551934 hemagglutinin protein HagA 34540040 2551409 lipoprotein, putative 34540364 2552375 extracellular protease, putative 34541613 2552074 hemagglutinin protein HagE 34540183 2551891 internalin-related protein Shigella flexneri 2a str. 2457T 30065424 1080663 minor fimbrial subunit, D-mannose specific adhesin 30062726 1077662 putative adhesion and penetration protein 30063758 1078834 putative fimbrial-like protein 30065431 1080671 major type 1 subunit fimbrin (pilin) 30063366 1078379 flagellar protein FliD 30064308 1079668 outer membrane fluffing protein 30062613 1077555 flagellar hook protein FlgE 30061954 1076843 conserved hypothetical lipoprotein 30065173 1080393 putative lipase 30065425 1080664 minor fimbrial subunit, precursor polypeptide 30064485 1079637 putative fimbrial protein 30062615 1077558 flagellar basal body L-ring protein FlgH 30064307 1079452 outer membrane fluffing protein 30065601 1080859 putative glycoprotein/receptor 30062118 1077025 putative fimbrial-like protein 30064099 1079223 lipoprotein 30062616 1077559 flagellar basal body P-ring protein FlgI 30063546 1078596 putative fimbrial-like protein 30062940 1077910 putative outer membrane protein 30065426 1080665 minor fimbrial subunit, precursor polypeptide 30062779 1077721 putative outer membrane protein 30064194 1079329 putative lipoprotein 30063365 1078378 flagellin 30062298 1077222 outer membrane protein X 30064968 1080175 putative major fimbrial subunit 30061858 1076740 outer membrane pore protein E (E, Ic, NmpAB) 30062178 1080410 minor lipoprotein 30062479 1077412 putative fimbrial-like protein 30062565 1077506 minor curlin subunit precursor 30063880 1078972 putative outer membrane lipoprotein 30064531 1079686 cytoplasmic membrane protein 30065033 1080243 putative receptor protein Streptococcus mutans UA159 24378550 1029610 putative secreted antigen GbpB/SagA; putative peptidoglycan hydrolase 24379087 1028055 cell surface antigen SpaP 24380463 1029310 putative membrane protein 24379075 1028046 penicillin-binding protein 2b 24378955 1027967 penicillin-binding protein 1a; membrane carboxypeptidase 24379801 1028662 glucan-binding protein C, GbpC 24379528 1029536 hypothetical protein; possible cell wall protein, WapE 24379231 1028158 putative glucan-binding protein D; BglB-like protein 24380488 1029325 conserved hypothetical protein; possible transmembrane protein 24380291 1029139 putative amino acid binding protein 24379342 1028247 putative penicillin-binding protein, class C; fmt-like protein 24380047 1028904 putative ABC transporter, branched chain amino acid-binding protein 24378698 1029755 putative ABC transporter, metal binding lipoprotein; surface adhesin precursor; saliva-binding protein; lipoprotein receptor LraI (LraI family) 24378708 1029768 putative transfer protein 24379427 1028331 cell wall-associated protein precursor WapA 24379272 1028196 putative amino acid transporter, amino acid-binding protein 24379641 1028511 putative ABC transporter, amino acid binding protein Streptococcus pneumoniae R6 15902395 934801 Choline-binding protein 15902381 934810 Choline-binding protein F 15902165 932894 Surface protein pspA precursor 15904047 934859 Choline binding protein D 15904036 933487 Choline binding protein A 15903986 933069 Choline-binding protein 15903796 933669 Autolysin (N-acetylmuramoyl-L-alanine amidase) Neisseria meningitidis Z2491 15794121 907145 putative membrane protein 15794144 907168 putative surface fibril protein 15793284 906275 truncated pilin 15793460 906456 IgA-specific serine endopeptidase 15793282 906273 fimbrial protein precursor (pilin) 15793337 906332 adhesin 15793253 906243 putative lipoprotein 15794356 907848 putative lipoprotein 15793684 906699 putative membrane protein 15793290 906281 truncated pilin 15793283 906274 truncated pilin 15793475 906471 haemoglobin-haptoglobin-utilization protein 15793406 906401 porin, major outer membrane protein P.I 15794985 907333 adhesin MafA2 15794344 907836 putative lipoprotein 15794622 908118 hypothetical outer membrane protein 15793599 906604 pilus-associated protein 15793763 906779 putative periplasmic binding protein Streptococcus pyogenes MGAS8232 19745214 995235 putative secreted protein 19746570 994224 putative penicillin-binding protein 1a 19745593 994771 putative 42 kDa protein 19745813 993958 putative adhesion protein 19745225 994839 putative choline binding protein 19745828 995250 streptolysin S associated protein 19746229 995021 putative minor tail protein 19746909 994105 putative laminin adhesion 19745560 995061 putative cell envelope proteinase Treponema pallidum subsp. pallidum str. Nichols 15639714 2611034 flagellar hook protein (flgE) 15639609 2611657 tpr protein J (tprJ) 15639111 2610909 tpr protein C (tprC) 15639125 2610968 tpr protein D (tprD) SARS coronavirus 31581505 spike protein S [SARS coronavirus Frankfurt 1] 32187357 spike protein S [SARS coronavirus HSR 1] 32187342 spike glycoprotein [SARS coronavirus ZJ01] 30698329 putative spike glycoprotein S [SARS coronavirus TW1] 30421454 putative spike glycoprotein [SARS coronavirus CUHK-Su10] 30027620 S protein [SARS coronavirus Urbani] 29836496 1489668 E2 glycoprotein precursor; putative spike glycoprotein [SARS coronavirus] 30795145 spike glycoprotein [SARS coronavirus Tor2] 31416295 spike glycoprotein S [SARS coronavirus GD01] 30023954 putative E2 glycoprotein precursor [SARS coronavirus CUHK-W1] 30275669 spike glycoprotein S [SARS coronavirus BJ01] 29837498 3C-like proteinase nsp5-pp1a/pp1ab (3CL-PRO) [SARS coronavirus] 29837501 putative nsp8-pp1a/pp1ab [SARS coronavirus] 29837503 putative nsp10-pp1a/pp1ab; formerly known as growth-factor- like protein [SARS coronavirus] 29837502 putative nsp9-pp1a/pp1ab [SARS coronavirus]

TABLE 6 Hypothetical proteins predicted as putative adhesins by SPAAN in the genomes listed in table 2 - (Total number of proteins = 105) Protein GI number Gene ID Escherichia coli O157:H7 13363955 915578 13360000 914929 13362244 912369 13359999 914888 13361583 917316 13361172 913156 13361131 913207 13359780 914422 13360571 912499 13362197 912893 13362260 912399 13360947 913505 13361464 917196 13361635 917367 13362421 916655 13361463 917195 Haemophilus influenzae Rd 16272115 951058 30995442 950581 Helicobacter pylori J99 4155526 889586 4155712 889748 4155632 889684 4156035 889468 4155499 Mycoplasma pneumoniae 13507870 877230 13508239 877245 13508109 876868 13508025 877084 13507838 876784 13507883 877183 13507871 877239 13507944 877056 13508241 876750 13507942 877055 13507840 877387 13507867 877242 13508201 877044 13507941 876985 13508114 877397 Mycobacterium tuberculosis H37Rv 15611014 886198 15610173 887320 15609513 885515 15608094 885411 15610958 886155 15607528 886436 15607678 887473 15609587 885760 15610708 887227 15609526 885246 15611033 886225 15609028 885094 15607730 887771 15609121 885813 15608255 885951 15608409 887039 15609124 885815 15607734 887797 Rickettsia prowazekii strain Madrid E 15604649 883964 15604322 883472 15604659 883996 15604417 883217 Porphyromonas gingivalis W83 34540233 2551594 Shigella flexneri 2a str. 2457T 30062687 1077638 30062956 1080449 30063681 1078754 30065435 1080675 30063891 1078983 30063211 1078195 30065233 1080463 30064387 1079531 30062638 1077590 30065236 1080466 30061839 1076721 Streptococcus mutans UA159 24378864 1029452 24380475 1029319 24380237 1029088 24379203 1028139 24380480 1029320 24379275 1029489 24379291 1028216 24379295 1028215 24379804 1028663 24379162 1029417 24378987 1029363 24379179 1028118 24379166 1028107 24378827 1029444 24380216 1029067 Streptococcus pneumoniae R6 15902140 932867 15903446 934616 15903916 934001 15903848 933609 15902832 934332 15902372 934804 15902152 932889 Neisseria meningitidis Z2491 15793668 906680 15794714 907603 Streptococcus pyogenes MGAS8232 19747011 993608 19747024 994165 19747012 994373 19746396 995057 19746651 993824 19745883 995045 19745912 994077 Treponema pallidum subsp. pallidum str. Nichols 15639844 2611061 15639720 2611059

TABLE 7 The list of 198 adhesins found in bacteria PapG (E. coli) 12837502 7407201 7407207 7407205 147096 4240529 7407203 42308 7443327 78746 18265934 26111419 26250987 26109826 26249418 13506767 42301 78745 129622 147092 13506906 7407209 147080 281926 7407199 147100 78744 SfaS (E. coli) 477910 264035 42959 134449 96425 FimH (E. coli) 26251208 26111640 5524634 29422425 5524630 29422435 29422415 10946257 29422419 11120564 29422457 11120562 29422459 5524632 29422455 29422453 29422451 29422449 29422447 29422445 29422443 29422437 29422433 29422431 29422429 29422427 29422423 29422421 29422417 729494 1361011 1790775 3599571 29422441 12620398 29422439 5524628 1787779 1742472 1742463 15801636 25321294 12515169 11120566 24051859 24112911 13360484 15800801 15830279 25392018 25500156 12514120 1787173 16128908 16501811 16759519 24051219 24112354 30040724 30062478 6650093 5524636 1778448 Intimin (E. coli) 17384659 4388530 1389879 15723931 4323336 4323338 4323340 4323342 4323344 4323346 4323348 4689314 PrsG (E. coli) 42523 42529 7443328 7443329 1172645 HMW1 (Nontypeable H. influenzae) 282097 HMW2 (Nontypeable H. influenzae) 5929966 Hia (Nontypeable H. influenzae) 25359682 25359489 25359709 25359628 25359414 25359389 21536216 25359445 HifE (H. influenzae) 13506868 13506870 13506872 13506874 13506876 3688787 3688790 3688793 2126301 1170264 1170265 533127 535169 3025668 3025670 3025672 3025674 642038 MrkD (K. pneumoniae) 127307 FHA (B. pertussis) 17154501 Pertactin (B. pertussis) 33571840 YadA (Y. enterocolitica) 10955604 4324391 28372996 23630568 32470319 SpaP (S. mutans) 26007028 47267 PAc (S. mutans) 129552 SspA (Streptococcus gordonii) 25990270 1100971 CshA (Streptococcus gordonii) 457707 CshB (Streptococcus gordonii) 18389220 ScaA (Streptococcus gordonii) 310633 SspB (Streptococcus gordonii) 25055226 3220006 SpaA (Streptococcus sobrinus) 546643 PAg (Streptococcus sobrinus) 217036 47561 Protein F (Streptococcus pyogenes) 19224134 PsaA (Streptococcus pneumoniae) 18252614 7920456 7920458 7920460 7920462 CbpA^(e)/SpsA/PbcA/PspC 14718654 (Streptococcus pneumoniae) 2425109 2576331 2576333 3153898 9845483 19548141 FimA (Streptococcus parasanguis) 97883 SsaB (Streptococcus sanguis) 97882 EfaA (Enterococcus faecalis) 493017 FnbA (Staphylococcus aureus) 120457 FnbB (Staphylococcus aureus) 581562 21205592 13702452 BabA (Helicobacter pylori) 13309962 13309964 13309966 13309968 13309970 13309972 13309974 13309976 13309978 13309980 13309982 13309984 13309986 13309988 13309990 13309992 13309994 Advantages:

1. The method helps in discovering putative adhesins, which are of great importance in drug discoveries and preventive therapeutics.

2. The method is useful in predicting the adhesive nature of even unique proteins, because it is independent of the homology of the query proteins with other proteins.

3. This method is easy to use. For calculating the output, only the amino acid sequence is required as input. No other information is required to get the information about its adhesive nature.

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1. A computational method for identifying adhesin and adhesin-like proteins, said method comprising steps of: a. computing the sequence-based attributes of protein sequences using five attribute modules of a neural network software, wherein the attributes are, (i) amino acid frequencies, (ii) multiplet frequency, (iii) dipeptide frequencies, (iv) charge composition, and (v) hydrophobic composition, b. training a artificial neural Network (ANN) for each of the computed five attributes, and c. identifying the adhesin and adhesin-like proteins having probability of being an adhesin (P_(ad)) as ≧0.51.
 2. A method as claimed in claim 1, wherein the protein sequences are obtained from pathogens, eukaryotes, and multicellular organisms.
 3. A method as claimed in claim 1, wherein the protein sequences are obtained from the pathogens selected from a group of organisms comprising Escherichia coli, Haemophilus influenzae, Helicobacter pylori, Mycoplasma pneumoniae, Mycobacterium tuberculosis, Rickettsiae prowazekii, Porphyromonas gingivalis, Shigella flexneri, Streptococcus mutans, Streptococcus pneumoniae, Neisseria meningitides, Streptococcus pyogenes, Treponema pallidum and Severe Acute Respiratory Syndrome associated human coronavirus (SARS ).
 4. A method as claimed in claim 1, wherein the method is a non-homology method.
 5. A method as claimed in claim 1, wherein the method uses 105 compositional properties of the sequences.
 6. A method as claimed in claim 1, wherein the method shows sensitivity of at least 90%.
 7. A method as claimed in claim 1, wherein the method shows specificity of 100%.
 8. A method as claimed in claim 1, wherein the method helps identifies adhesins from distantly related organisms.
 9. A method as claimed in claim 1, wherein the neural network has multi-layer feed forward topology, consisting of an input layer, one hidden layer, and an output layer.
 10. A method as claimed in claim 9, wherein the number of neurons in the input layer are equal to the number of input data points for each attribute.
 11. A method as claimed in claim 1, wherein the “P_(ad)” is a weighted linear sum of the probabilities from five computed attributes.
 12. A method as claimed in claim 1, wherein each trained network assigns a probability value of being an adhesin for the protein sequence.
 13. A computer system for performing the method of claim 1, said system comprising a central processing unit, executing SPAAN program, giving probabilities based on different attributes using Artificial Neural Network and in built other programs of assessing attributes, all stored in a memory device accessed by CPU, a display on which the central processing unit displays the screens, of the above mentioned programs in response to user inputs; and a user interface device.
 14. A set of 274 annotated genes encoding adhesin and adhesin-like proteins having SEQ ID Nos. 385 to
 658. 15. A set of 105 hypothetical genes encoding adhesin and adhesin-like proteins, having SEQ ID Nos. 659, to
 763. 16. A set of 279 annotated adhesin and adhesin-like proteins of SEQ ID Nos. 1 to
 279. 17. A set of 105 hypothetical adhesin and adhesin-like proteins of SEQ ID Nos. 280 to
 384. 18. A fully connected multilayer feed forward Artificial Neural Network based on the computational method as claimed in claim 1, comprising of an input layer, a hidden layer and an output layer which are connected in the said sequence, wherein each neuron is a binary digit number and is connected to each neuron of the subsequent layer for identifying adhesin or adhesin like proteins, wherein the program steps comprise: [a] feeding a protein sequence in FASTA format; [b] processing the sequence obtained in step [a] through the 5 modules named A, C, D, H and M, wherein attribute A represents an amino acid composition, attribute C represents a charge composition, attribute D represents a dipeptide composition of the 20 dipeptides [NG, RE, TN, NT, GT, TT, DE, ER, RR, RK, RI, AT, TS, IV, SG, GS, TG, GN, VI and HR], attribute H represents a hydrophobic composition and attribute M represents amino acid frequencies in multiplets to quantify 5 types of compositional attributes of the said protein sequence to obtain numerical input vectors respectively for each of the said attributes wherein the sum of numerical input vectors is 105; [c] processing of the numerical input vectors obtained in step [b] by the input neuron layer to obtain signals, wherein the number of neurons is equal to the number of numerical input vectors for each attribute; [d] processing of signals obtained from step [c] by the hidden layer to obtain synaptic weighted signals, wherein the optimal number of neurons in the hidden layer was determined through experimentation for minimizing the error at the best epoch for each network individually; [e] delivering synaptic weighted signals obtained from step [d] to the output layer for assigning of a probability value for each protein sequence fed in step [a] as being an adhesin by each network module; and [f] using the individual probabilities obtained from step [e] for computing the final probability of a protein sequence being an adhesin denoted by the P_(ad) value, which is a weighted average of the individual probabilities obtained from step [e] and the associated fraction of correlation which is a measure of the strength of the prediction.
 19. A network as claimed in claim 18, wherein the input neuron layer consists of a total of 105 neurons corresponding to 105 compositional properties.
 20. A network as claimed in claim 18, wherein the hidden layer comprises of neurons represented as 30 for amino acid frequencies, 28 for multiplet frequencies, 28 for dipeptide frequencies, 30 for charge composition and 30 for hydrophobic composition.
 21. A network as claimed in claim 18, wherein the output layer comprises of neurons to deliver the output values as probability value for each protein sequence. 